docs: add workflow research, architecture, strategy, and critique

Generated by the planning workflow: validated API findings (Gemini/LiveKit/ GitHub/DO), full architecture synthesis, winning strategy, and red-team critique. Canonical source files staged under docs/generated/files/. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-27 14:31:25 -07:00
parent da5b2622b2
commit 2d7adea8e5
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+import { MongoClient } from 'mongodb';
+
+const uri = process.env.MONGODB_URI!;
+const DB = 'podman';
+
+async function main() {
+  const client = new MongoClient(uri);
+  await client.connect();
+  const db = client.db(DB);
+
+  await db.collection('pods').createIndex({ id: 1 }, { unique: true });
+  // High-volume observations expire after 6h to keep the cluster light.
+  await db.collection('observations').createIndex({ observedAt: 1 }, { expireAfterSeconds: 21600 });
+  await db.collection('observations').createIndex({ podId: 1, engineerId: 1 });
+  await db.collection('collisions').createIndex({ podId: 1, detectedAt: -1 });
+  await db.collection('interventions').createIndex({ id: 1 }, { unique: true });
+  await db.collection('team_model').createIndex({ podId: 1 }, { unique: true });
+  await db.collection('policy').createIndex({ pattern: 1 }, { unique: true });
+
+  // Atlas Vector Search index for collision-pattern recall (Voyage voyage-3 = 1024 dims).
+  try {
+    await db.command({
+      createSearchIndexes: 'memory_vectors',
+      indexes: [
+        {
+          name: 'vector_index',
+          type: 'vectorSearch',
+          definition: {
+            fields: [{ type: 'vector', path: 'embedding', numDimensions: 1024, similarity: 'cosine' }],
+          },
+        },
+      ],
+    });
+  } catch (e) {
+    console.warn('vector index (create in Atlas UI if this errors):', (e as Error).message);
+  }
+
+  console.log('PodMan DB initialized.');
+  await client.close();
+}
+main().catch((e) => { console.error(e); process.exit(1); });